闫 坤, 杨慧敏, 王德彩. 基于NDVI时间序列特征的森林土壤有机碳数字制图−以济源南山林场为例[J]. 土壤通报, 2024, 55(4): 921 − 931. DOI: 10.19336/j.cnki.trtb.2023120402
引用本文: 闫 坤, 杨慧敏, 王德彩. 基于NDVI时间序列特征的森林土壤有机碳数字制图−以济源南山林场为例[J]. 土壤通报, 2024, 55(4): 921 − 931. DOI: 10.19336/j.cnki.trtb.2023120402
YAN Kun, YANG Hui-min, WANG De-cai. Digital Mapping of Forest Soil Organic Carbon Based on NDVI Time Series Features:A Case Study of Jiyuan Nanshan Forest Farm[J]. Chinese Journal of Soil Science, 2024, 55(4): 921 − 931. DOI: 10.19336/j.cnki.trtb.2023120402
Citation: YAN Kun, YANG Hui-min, WANG De-cai. Digital Mapping of Forest Soil Organic Carbon Based on NDVI Time Series Features:A Case Study of Jiyuan Nanshan Forest Farm[J]. Chinese Journal of Soil Science, 2024, 55(4): 921 − 931. DOI: 10.19336/j.cnki.trtb.2023120402

基于NDVI时间序列特征的森林土壤有机碳数字制图以济源南山林场为例

Digital Mapping of Forest Soil Organic Carbon Based on NDVI Time Series Features:A Case Study of Jiyuan Nanshan Forest Farm

  • 摘要:
    目的 探究归一化植被指数(NDVI)的时间序列特征能否有效提升数字土壤空间制图的精度,为数字土壤制图研究提供新的思路。
    方法 以济源南山林场作为研究区,将地形因子、气候因子、植被因子,同时提取NDVI的时间序列特征作为环境协变量,采用普通克里格、回归克里格、随机森林、决策树、支持向量机等方法构建土壤有机碳预测模型,检验NDVI长时间序列数据能否有效地提高有机碳的制图精度。
    结果 在不同模型中,高程、地形湿度指数等地形因子在土壤有机碳制图中占主导地位;NDVI的时间序列特征在各模型的相对重要性均高于单时相植被因子。引入NDVI的时间序列特征,回归克里格模型、随机森林模型、支持向量机模型、决策树模型等的平均误差(ME)、均方根误差(RMSE)均呈现降低趋势,拟合决定系数(R2)与一致性指数(CCC)均呈现升高趋势。在不同模型中,引入时间序列的随机森林模型制图精度和模型可靠性改善最为显著,是本研究的最优模型(R2提高69.57%,ME降低88.04%)。
    结论 引入NDVI时间序列特征变量能够不同程度地降低模型的预测误差,提高模型的可靠性。研究为土壤有机碳制图提供了新的环境变量,为数字土壤制图提供了新的可能途径。

     

    Abstract:
    Objective The aim was to explore the time series characteristics of normalized vegetation index could effectively improve the accuracy of digital soil mapping, in order to provide new ideas for digital soil mapping research.
    Method Taking Jiyuan Nanshan Forest Farm as the study area, terrain factors, climate factors, vegetation factors, and time series characteristics of normalized difference vegetation index (NDVI) were simultaneously extracted as environmental covariates. Ordinary kriging, regression kriging, random forest, decision tree, support vector machine, and other methods were used to construct soil organic carbon (SOC) prediction models to test whether the long-term time series data of NDVI could effectively improve the mapping accuracy of SOC.
    Results Among different models, elevation, and terrain wetness index played dominant roles in SOC mapping. The time series characteristics of NDVI were consistently more important than single-phase vegetation factors in all models. The introduction of time series features of NDVI resulted in a decrease in mean error (ME) and root mean square error (RMSE) and an increase in the coefficient of determination (R2) and concordance correlation coefficient (CCC) for models such as RK, RF, SVM, and CART. Among different models, the random forest model incorporating time series has the most significant improvement in mapping accuracy and model reliability, making it the optimal model for this study. (R2 increased by 69.57%, ME decreased by 88.04%).
    Conclusion The introduction of NDVI time series features as variables can effectively reduce the prediction errors of models to varying degrees, enhancing the reliability of the models. This result provides new environmental variables for SOC mapping and opens up new possibilities for digital soil mapping.

     

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